Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Unlabelled text mining methods based on two extension models of concept lattices

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Concept lattice is a useful tool for text extraction. The common text clustering method fails to generate hierarchical relationships among categories and realize soft clustering simultaneously, while the concept lattice ignores the negative correlation between an object subset and an attribute subset. Motivated by the problems, we propose unlabelled text mining methods based on fuzzy concept lattice and three-way concept lattice. Firstly, we excavate hierarchical text categories to construct a classification system based on fuzzy concept lattice, and the labelled samples are obtained by the word matching method. Then, we construct a three-way concept lattice to get positive and negative classification rules based on the labelled samples, and the classifier is constructed to predict the new samples. Finally, Sogou laboratory news corpus is used to evaluate the efficiency of text clustering and classification methods. The results demonstrate that the improved clustering method has a higher average cluster goodness than earlier procedures and the classification model based on three-way concept lattice achieves a higher accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Lewis DD (1992) Representation and learning in information retrieval. Dissertation, University of Massachusetts

  2. Yang WC, Wu QW, Cheng ZS (2017) Research on distributed text clustering based on frequent itemset. In: 36th Chinese Control Conference, pp. 5700–5705

  3. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46

    Google Scholar 

  4. Mccallum A, Nigam K (1998) Employing EM and Pool-Based active learning for text classification. In: Proceedings of the 15th international conference on machine learning, pp 350–358

  5. Kandola J, Shawe-Taylor J, Cristianini N (2002) Learning semantic similarity. In: NIPS, pp 673–680

  6. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) NATO Advanced Study Institutes Series. Springer, Berlin, pp 445–470

    Google Scholar 

  7. Ganter B, Wille R (1999) Formal concept analysis: mathematical foundations (Chap 1). Springer, New York

    MATH  Google Scholar 

  8. Tang J, He W, Zhang W, Fan L (2010) An algorithm of extracting classification rule based on classified concept lattice. In: Proceedings of the 2nd international workshop on database technology and applications, Wuhan, pp 1–4

  9. Xie Z, Liu Z (2000) Concept lattice and association rule discovery. J Comput Res Dev 37(12):1415–1421

    Google Scholar 

  10. Kumar CA (2012) Fuzzy clustering-based formal concept analysis for association rules mining. Appl Artif Intell 26(3):274–301

    MathSciNet  Google Scholar 

  11. Houari A, Ayadi W, Ben Yahia S (2018) A new FCA-based method for identifying biclusters in gene expression data. Int J Mach Learn Cybern 9(11):1879–1893

    Google Scholar 

  12. Kumar CA, Radavansky M, Annapurna J (2012) Analysis of vector space model, latent semantic indexing and formal concept analysis for information retrieval. Cybern Inf Technol 12(1):34–48

    Google Scholar 

  13. Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37(3):2696–2704

    Google Scholar 

  14. Kang X, Miao D, Lin G, Liu Y (2018) Relation granulation and algebraic structure based on concept lattice in complex information systems. Int J Mach Learn Cybern 9(11):1895–1907

    Google Scholar 

  15. Richards D, Compton P (1997) Combining formal concept analysis and ripple down rules to support reuse. In: Proceedings of Software Engineering Knowledge Engineering SEKE 1997, Madrid, Springer, Heidelberg

  16. Singh PK, Kumar CA, Gani A (2016) A comprehensive survey on formal concept analysis, its research trends and applications. Int J Appl Math Comput Sci 26(2):495–516

    MathSciNet  MATH  Google Scholar 

  17. Formica A (2019) Similarity reasoning in formal concept analysis: from one- to many-valued contexts. Knowl Inf Syst 60(2):715–739

    Google Scholar 

  18. Carpineto C, Romano G (1996) Information retrieval through hybrid navigation of lattice representations. Int J Hum Comput Stud 45(5):553–578

    Google Scholar 

  19. Hotho A, Staab S, Stumme G (2003) Explaining text clustering results using semantic structures. In: European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2003 (LNCS 2838), pp 217–228

    Google Scholar 

  20. Huang L (2005) Study on search results clustering based on formal concept analysis. Dissertation, Huazhong University

  21. Wang N, Li YS (2006) Text mining based on concept lattice. Comput Technol Dev 16(1):114–116

    Google Scholar 

  22. Xu HS (2012) Construction search engine based on formal concept analysis and association rule mining. Adv Eng Forum 6–7:625–630

    Google Scholar 

  23. Liu JJ (2013) Research on semantic information retrieval model based on concept lattice. Dissertation, Jilin University

  24. Pollandt S (1997) Fuzzy-Begriffe: formale begriffsanalyse unscharfer daten. Springer, Berlin

    MATH  Google Scholar 

  25. Bĕlohlávek R (2004) Concept lattices and order in fuzzy logic. Ann Pure Appl Logic 128(1–3):277–298

    MathSciNet  MATH  Google Scholar 

  26. Bĕlohlávek R (2005) What is a fuzzy concept lattice? In: Proceedings of international conference on rough sets, Fuzzy Sets, Data Mining and Granular Computing, pp 19–26

  27. Quan TT, Hui SC, Fong ACM, Cao TH (2004) Automatic generation of ontology for scholarly semantic web. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol 3298. Springer, Berlin, pp. 726–740

    Google Scholar 

  28. Zadeh LA (1975) Fuzzy logic and approximate reasoning (in memory of Grigore Moisil). Synthese 30(3–4):407–428

    Google Scholar 

  29. Zou CF, Deng HF, Wan JF, Wang ZR, Deng P (2018) Mining and updating association rules based on fuzzy concept lattice. Future Gener Comp Syst 82:689–706

    Google Scholar 

  30. Ravi K, Vadlamani R, Prasad PSRK (2017) Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl Soft Comput 60:786–807

    Google Scholar 

  31. Guarino N, Oberle D, Staab S (2010) What is an ontology? In: Staab S, Studer R (eds) Handbook on ontologies. Springer, Berlin, pp 1–17

    Google Scholar 

  32. Yang Q, Chen W, Wen B (2010) Fuzzy ontology model for semantic information query. Comput Eng 36(8):188–190

    Google Scholar 

  33. Li HL, Liu N, Li GY (2012) Concept distance clustering method of generating fuzzy ontology. Comput Eng Des 33(4):1537–1538

    MathSciNet  Google Scholar 

  34. Maio CD, Fenza G, Loia V, Senatore S (2012) Hierarchical web resources retrieval by exploiting fuzzy formal concept analysis. Inf Process Manage 48(3):399–418

    Google Scholar 

  35. Chen RC, Bau CT, Yeh CJ (2011) Merging domain ontologies based on the WordNet system and fuzzy formal concept analysis techniques. Appl Soft Comput 11(2):1908–1923

    Google Scholar 

  36. Priya M, Kumar CA (2015) A survey of state of the art of Ontology construction and merging using formal concept analysis. Indian J Sci Technol 8(24):1–7

    Google Scholar 

  37. Bobillo F, Straccia U (2015) The fuzzy ontology reasoner fuzzyDL. Knowl-Based Syst 95:12–34

    Google Scholar 

  38. Macqueen J (1965) Some methods for classification and analysis of multiVariate observations. In: Proceedings of the 5th berkeley symposium on mathematical statistics and probability, pp 281–297

  39. Bezdek JC (1980) A convergence theorem for the fuzzy isodata clustering algorithms. IEEE Trans Patt Anal Machine IntelL 2(1):1–8

    MathSciNet  MATH  Google Scholar 

  40. Gao CF, Wu XJ, Zhang SS (2010) An improved semi-supervised fuzzy clustering algorithm. Control Decis 25:115–120

    Google Scholar 

  41. Huang JB, Ji HB (2005) A web search results clustering algorithm based on fuzzy concept lattices. J Xidian Univ 32(6):856–860

    Google Scholar 

  42. Zhou W, Liu Z, Zhao Y (2007) Ontology learning by clustering based on fuzzy formal concept analysis. In: Proceedings of the 31st annual international computer software and applications conference, pp 204–210

  43. Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining (KDD’98), pp 80–86

  44. Wang XZ, Xing HJ, Li Y et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Google Scholar 

  45. Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715

    MathSciNet  Google Scholar 

  46. Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475

    Google Scholar 

  47. Wang XZ, Zhang TL, Wang R (2019) Noniterative deep learning: incorporating restricted Boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst 49(7):1299–1308

    Google Scholar 

  48. Sahami M (1995) Learning classification rules using lattices (extended abstract). In: Proceedings of the 8th European conference on machine learning, pp 343–346

    Google Scholar 

  49. Gupta, A., Kumar, N., Bhatnagar (2005) Incremental classification rules based on association rules using formal concept analysis. In: Perner, P., Imiya, A. (eds.) MLDM. LNCS (LNAI), vol 3587. Springer, Berlin, pp. 11–20

    Google Scholar 

  50. Wang J, Liang J, Qian Y (2011) Closed-Label concept lattice based rule extraction approach. In: International conference on intelligent computing: bio-Inspired computing and applications, pp 690–698

    Google Scholar 

  51. Qi J, Wei L, Yao Y (2014) Three-Way formal concept analysis. Lect Notes Comput Sci 8818:732–741

    MATH  Google Scholar 

  52. Wei L, Qian T (2015) The three-way object oriented concept lattice and the three-way property oriented concept lattice. In: International conference on machine learning and cybernetics, pp 854–859

  53. Ren RS, Wei L (2016) The attribute reductions of three-way concept lattices. Knowl-Based Syst 99:92–102

    Google Scholar 

  54. Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl-Based Syst 91:143–151

    Google Scholar 

  55. Huang C, Li J, Mei C, Wu W (2017) Three-way concept learning based on cognitive operators: an information fusion viewpoint. Int J Approx Reason 83:218–242

    MathSciNet  MATH  Google Scholar 

  56. Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263

    Google Scholar 

  57. Mouliswaran SC, Kumar CA, Chandrasekar C (2018) Role based access control design using three-way formal concept analysis. Int J Mach Learn Cybern 9(11):1807–1837

    Google Scholar 

  58. Antonie ML, Zaïane OR (2004) Mining positive and negative association rules: an approach for confined rules. In: Principles and practice of knowledge discovery in databases, vol 3202, pp. 27–38

    Google Scholar 

  59. Sogou. Sogou Labs[DB/OL]. http://www.sogou.com/labs/resource/list_news.php.2017.

Download references

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772021, 11371014 and 61772420).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, China

    Xiaoyu Chen, Jianjun Qi & Xiaomin Zhu

  2. Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada

    Xin Wang

  3. School of Mathematics, Northwest University, Xi’an, China

    Zhen Wang

  4. School of Information Science and Technology, Northwest University, Xi’an, China

    Xin Wang

Authors
  1. Xiaoyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianjun Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaomin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianjun Qi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Qi, J., Zhu, X. et al. Unlabelled text mining methods based on two extension models of concept lattices. Int. J. Mach. Learn. & Cyber. 11, 475–490 (2020). https://doi.org/10.1007/s13042-019-00987-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-019-00987-6

Keywords

Search

Navigation